Event-Related Bias Removal for Real-time Disaster Events
This work addresses the challenge of real-time information filtering in social media for crisis response, though it appears incremental as it builds on prior bias removal methods.
The paper tackles the problem of classifying actionable posts in real-time during new disaster events by addressing event-specific biases in pre-trained models, resulting in improved performance on tweet importance classification.
Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful information requires rapid analysis of huge volume of data in real-time. This poses a complex problem due to the large amount of posts that do not contain any actionable information. Furthermore, the classification of information in real-time systems requires training on out-of-domain data, as we do not have any data from a new emerging crisis. Prior work focuses on models pre-trained on similar event types. However, those models capture unnecessary event-specific biases, like the location of the event, which affect the generalizability and performance of the classifiers on new unseen data from an emerging new event. In our work, we train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.